import os
os.getcwd()

'/content/drive/My Drive/ITP/Assignments/Neural Network'

os.chdir('/content/drive/MyDrive/ITP/Assignments/Neural Network')

os.getcwd()

'/content/drive/MyDrive/ITP/Assignments/Neural Network'

A communications equipment manufacturing company has a product which is responsible for emitting informative signals. Company wants to build a machine learning model which can help the company to predict the equipment’s signal quality using various parameters.

---

PROJECT OBJECTIVE: The need is to build a regressor which can use these parameters to determine the signal strength or quality

Import data

import numpy as np
import pandas as pd
df=pd.read_csv('Signal.csv')

Data analysis & visualisation

df.head()

	Parameter 1	Parameter 2	Parameter 3	Parameter 4	Parameter 5	Parameter 6	Parameter 7	Parameter 8	Parameter 9	Parameter 10	Parameter 11	Signal_Strength
0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5
1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5
3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6
4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5

df.shape

(1599, 12)

df.dtypes

Parameter 1        float64
Parameter 2        float64
Parameter 3        float64
Parameter 4        float64
Parameter 5        float64
Parameter 6        float64
Parameter 7        float64
Parameter 8        float64
Parameter 9        float64
Parameter 10       float64
Parameter 11       float64
Signal_Strength      int64
dtype: object

check null values

df.describe().transpose()

	count	mean	std	min	25%	50%	75%	max
Parameter 1	1599.0	8.319637	1.741096	4.60000	7.1000	7.90000	9.200000	15.90000
Parameter 2	1599.0	0.527821	0.179060	0.12000	0.3900	0.52000	0.640000	1.58000
Parameter 3	1599.0	0.270976	0.194801	0.00000	0.0900	0.26000	0.420000	1.00000
Parameter 4	1599.0	2.538806	1.409928	0.90000	1.9000	2.20000	2.600000	15.50000
Parameter 5	1599.0	0.087467	0.047065	0.01200	0.0700	0.07900	0.090000	0.61100
Parameter 6	1599.0	15.874922	10.460157	1.00000	7.0000	14.00000	21.000000	72.00000
Parameter 7	1599.0	46.467792	32.895324	6.00000	22.0000	38.00000	62.000000	289.00000
Parameter 8	1599.0	0.996747	0.001887	0.99007	0.9956	0.99675	0.997835	1.00369
Parameter 9	1599.0	3.311113	0.154386	2.74000	3.2100	3.31000	3.400000	4.01000
Parameter 10	1599.0	0.658149	0.169507	0.33000	0.5500	0.62000	0.730000	2.00000
Parameter 11	1599.0	10.422983	1.065668	8.40000	9.5000	10.20000	11.100000	14.90000
Signal_Strength	1599.0	5.636023	0.807569	3.00000	5.0000	6.00000	6.000000	8.00000

df.isnull().sum()

Parameter 1        0
Parameter 2        0
Parameter 3        0
Parameter 4        0
Parameter 5        0
Parameter 6        0
Parameter 7        0
Parameter 8        0
Parameter 9        0
Parameter 10       0
Parameter 11       0
Signal_Strength    0
dtype: int64

Data frame has no null values

from matplotlib import pyplot as plt
import seaborn as sns
sns.pairplot(df,hue='Signal_Strength')

<seaborn.axisgrid.PairGrid at 0x7f33844bfd68>

From the pairplot it is clear that none of the parameter is good for using a classification model

C_mat = df.corr()
fig = plt.figure(figsize = (15,15))
sns.heatmap(C_mat, vmax = .8, square = True)
plt.show()

C_mat

	Parameter 1	Parameter 2	Parameter 3	Parameter 4	Parameter 5	Parameter 6	Parameter 7	Parameter 8	Parameter 9	Parameter 10	Parameter 11	Signal_Strength
Parameter 1	1.000000	-0.256131	0.671703	0.114777	0.093705	-0.153794	-0.113181	0.668047	-0.682978	0.183006	-0.061668	0.124052
Parameter 2	-0.256131	1.000000	-0.552496	0.001918	0.061298	-0.010504	0.076470	0.022026	0.234937	-0.260987	-0.202288	-0.390558
Parameter 3	0.671703	-0.552496	1.000000	0.143577	0.203823	-0.060978	0.035533	0.364947	-0.541904	0.312770	0.109903	0.226373
Parameter 4	0.114777	0.001918	0.143577	1.000000	0.055610	0.187049	0.203028	0.355283	-0.085652	0.005527	0.042075	0.013732
Parameter 5	0.093705	0.061298	0.203823	0.055610	1.000000	0.005562	0.047400	0.200632	-0.265026	0.371260	-0.221141	-0.128907
Parameter 6	-0.153794	-0.010504	-0.060978	0.187049	0.005562	1.000000	0.667666	-0.021946	0.070377	0.051658	-0.069408	-0.050656
Parameter 7	-0.113181	0.076470	0.035533	0.203028	0.047400	0.667666	1.000000	0.071269	-0.066495	0.042947	-0.205654	-0.185100
Parameter 8	0.668047	0.022026	0.364947	0.355283	0.200632	-0.021946	0.071269	1.000000	-0.341699	0.148506	-0.496180	-0.174919
Parameter 9	-0.682978	0.234937	-0.541904	-0.085652	-0.265026	0.070377	-0.066495	-0.341699	1.000000	-0.196648	0.205633	-0.057731
Parameter 10	0.183006	-0.260987	0.312770	0.005527	0.371260	0.051658	0.042947	0.148506	-0.196648	1.000000	0.093595	0.251397
Parameter 11	-0.061668	-0.202288	0.109903	0.042075	-0.221141	-0.069408	-0.205654	-0.496180	0.205633	0.093595	1.000000	0.476166
Signal_Strength	0.124052	-0.390558	0.226373	0.013732	-0.128907	-0.050656	-0.185100	-0.174919	-0.057731	0.251397	0.476166	1.000000

Parameters 1, 3, 8, 9 have high correlation

Parameters 2, 3 have moderate correlation

Parameters 3 , 9 have moderate correlation

Parameters 6, 7 have high correlation

Hence Parameter 1,2,3,6,7,8,9 can be used to build the model while parameters 4,5,10,11 can be droppped since they exhibit low correlation

from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error  
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler

X=df[['Parameter 1','Parameter 2','Parameter 3','Parameter 6','Parameter 7','Parameter 8','Parameter 9']]
y=df['Signal_Strength']
X_train,X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=23)

X_train.shape

(1279, 7)

y_train.shape

(1279,)

NN_model = Sequential()

# The Input Layer :
NN_model.add(Dense(30,activation='relu', input_dim = X_train.shape[1]))

# The Output Layer :
NN_model.add(Dense(1))

# Compile the network :

NN_model.compile(loss='mean_squared_error', optimizer='adam')
NN_model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 30)                240       
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 31        
=================================================================
Total params: 271
Trainable params: 271
Non-trainable params: 0
_________________________________________________________________

NN_model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split = 0.2)

X_test.shape

import numpy as np
test_x = np.reshape([9, 0.64, 0.19, 60, 160,0.9998,3.18 ],(-1, 7))

test_y = NN_model.predict(test_x)

print(test_y)

PROJECT OBJECTIVE: The need is to build a classifier which can use these parameters to determine the signal strength or quality [as number].

df=pd.read_csv('Signal.csv')

Add classification 1 for Signal strength > 5 meaing good quality and 0 for strength <=5

df['quality']=np.where(df['Signal_Strength']>5,1,0)
df.head()
    

df.dtypes

df.describe().transpose()

sns.pairplot(df,hue='quality')

<seaborn.axisgrid.PairGrid at 0x7f336a013860>

X_cls=df.iloc[:,0:10]
y_cls=df['quality']
X_cls_train,X_cls_test, y_cls_train, y_cls_test=train_test_split(X_cls,y_cls,test_size=0.2,random_state=23)

NN_model2 = Sequential()

# The Input Layer :
NN_model2.add(Dense(30,activation='relu', input_dim = X_cls_train.shape[1]))

# The Hidden Layer :
NN_model2.add(Dense(30,activation='relu'))

# The Output Layer :
NN_model2.add(Dense(2, activation='tanh'))

# Compile the network :

NN_model2.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
NN_model2.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_2 (Dense)              (None, 30)                330       
_________________________________________________________________
dense_3 (Dense)              (None, 30)                930       
_________________________________________________________________
dense_4 (Dense)              (None, 2)                 62        
=================================================================
Total params: 1,322
Trainable params: 1,322
Non-trainable params: 0
_________________________________________________________________

NN_model2.fit(X_cls_train, y_cls_train, epochs=100, batch_size=32, validation_split = 0.2)

X_new=X_cls_test[:11]
y_proba=NN_model2.predict(X_new)
y_proba.round(2)

y_pred=NN_model2.predict_classes(X_new)
y_pred

Part 3: Building GUI

import tkinter as tk

from tkinter import ttk
import pandas as pd
import re

get_ipython().system_raw('Xvfb :1 -screen 0 1600x1200x16  &')
!apt-get install -y xvfb # Install X Virtual Frame Buffer
import os
os.system('Xvfb :1 -screen 0 1600x1200x16  &')    # create virtual display with size 1600x1200 and 16 bit color. Color can be changed to 24 or 8
os.environ['DISPLAY']=':1.0'    # tell X clients to use our virtual DISPLAY :1.0

# App Window
win= tk.Tk()
win.title('ANN Regression & Classification Automation GUI')

# Step 1: Import data from frame name------------------
Name=ttk.Label(win,text="Step 1: File Name")
Name.grid(row=0,column=0,sticky=tk.W)
Name_var=tk.StringVar()
Name_entrybox=ttk.Entry(win,width=16, textvariable=Name_var)
Name_entrybox.grid(row=0,column=1)

def Import_Data():
  global DB
  DF_Name=Name_var.get()
  DB_extension=re.findall("\..*",DF_Name)
  if DB_extension==['.xlsx']:
    DB=pd.read_excel(DF_Name)
  elif DB_extension==['.csv']:
    DB_extension=pd.read_csv(DF_Name)
    #Blank empty window to print confirmation
    confirm='Done'
    Confirm_entrybox=ttk.Entry(win,width=16)
    Confirm_entrybox.grid(row=0,column=3)
    Confirm_entrybox.insert(1,str(confirm))

  
Import_Data_Button=ttk.Button(win,text='Import Data',command=Import_Data)
Import_Data_Button.grid(row=0,column=2)




#Step 2: Target data frame name
Target=ttk.Label(win.text="Step 2: Target Column")
Target.grid(row=1,column=0,sticky=tk.W)
Target_var=tk.StringVar()
Target_entrybox=ttk.Entry(win,width=16, textvariable=Target_var)
Target_entrybox.grid(row=1, column=1)

def Target_Data():
  global DB
  DF_Name=Name_var.get()
  DB_extension=re.findall("\..*",DF_Name)
  if DB_extension==['.xlsx']:
    DB=pd.read_excel(DF_Name)
  elif DB_extension==['.csv']:
    DB_extension=pd.read_csv(DF_Name)
    #Blank empty window to print confirmation
    confirm='Done'
    Confirm_entrybox=ttk.Entry(win,width=16)
    Confirm_entrybox.grid(row=2,column=3)
    Confirm_entrybox.insert(1,str(confirm))

  
Import_Data_Button=ttk.Button(win,text='Target Data',command=Target_Data)
Import_Data_Button.grid(row=2,column=2)
  

#Step 3: Predict data frame name
Prediction=ttk.Label(win.text="Step 3: Predict Column")
Prediction.grid(row=2,column=0,sticky=tk.W)
Prediction_var=tk.StringVar()
Prediction_entrybox=ttk.Entry(win,width=16, textvariable=Predict_var)
Prediction_entrybox.grid(row=2, column=1)

def ANN_Predict():
  ANN_Future['Prediction']=NN_model2.predict(ANN_Future)
  ANN_Future.to_csv('Prediction.csv')

ANN_Button=ttk.Button(win,text="ANN",command=ANN_Predict)
ANN_Button.grid(row=2,column=0)
  
win.mainloop()